Author

Aldwin Arambulo (SID 510575452) | Mia Childers (SID 510414892) | Mu-Wei Chung (SID 510611635)

Published

July 24, 2024

Code

library(tidyverse)
library(ggplot2)
library(plotly)
library(rmarkdown)

Text here

This report investigates the impacts of vaccinations amongst the different development regions as we dived into the difference of life expectancy and the global coverage of the vaccination programs. We used a dataset from OWID (Our World In Data) for vaccination coverage and global life expectancy. We analysed the trend of the vaccination programs in different regions to deduce the impact of the vaccines and potential life expectancy gains.

Life Expectancy Data

Life Expectancy dataset from Our World in Data.

Code

le_df <- read_csv("data/life-expectancy.csv")
paged_table(le_df)

Cleaning life expectancy data

Code

le_clean <- janitor::clean_names(le_df)
# rename column
le_clean <- le_clean %>% 
  rename(period_life_expect = period_life_expectancy_at_birth_sex_all_age_0)
paged_table(le_clean)

Global Vaccination Coverage Data

Code

vaccination_df <- read_csv("data/global-vaccination-coverage.csv")
vaccination_df %>% paged_table()

Cleaning vaccination data

Code

vaccination_clean <- janitor::clean_names(vaccination_df) %>% paged_table()
vaccination_clean

Using a combination of Pearson’s correlation coefficient and linear regression, the vaccines for one dose of Salk’s polio vaccine, one dose of the meningococcal vaccine, three doses of the polio vaccine and three doses of the DPT (diphtheria, pertussis, tetanus) vaccine were found to be the most significant in increasing life expectancy globally.

Code

merged_datasets = merge(vaccination_clean, le_clean, by=c("entity","year"))
names(merged_datasets)[names(merged_datasets) == 'period_life_expectancy_at_birth_sex_all_age_0'] <- 'life_expectancy'

References

--- title: "![OWID Vaccine Data](imgs/Winter_Data_Science_Challenge_2024_banner.png)" author: "Aldwin Arambulo (SID 510575452) | Mia Childers (SID 510414892) | Mu-Wei Chung (SID 510611635)" date: "`r Sys.Date()`" format: html: fig-cap-location: bottom page-layout: full toc: false theme: superhero anchor-sections: true code-tools: true code-fold: true source: true code-copy: hover df-print: paged smooth-scroll: true html-math-method: katex self-contained: true self-contained-math: true code-link: true table-of-contents: false execute: warning: false editor_options: chunk_output_type: inline --- ################################################ <style> h1 { font-size: 20px; font-weight: bold; } h2 { font-size: 18px; font-weight: bold; } h3 { font-size: 16px; } } </style> ################################################ ```{r setup} #| warning: false #| message: false library(tidyverse) library(ggplot2) library(plotly) library(rmarkdown) ``` ::: panel-tabset # Executive Summary Text here # Background This report investigates the impacts of vaccinations amongst the different development regions as we dived into the difference of life expectancy and the global coverage of the vaccination programs. We used a dataset from [OWID](https://ourworldindata.org/) (Our World In Data) for vaccination coverage and global life expectancy. We analysed the trend of the vaccination programs in different regions to deduce the impact of the vaccines and potential life expectancy gains. # Data ## Life Expectancy Data [Life Expectancy](https://ourworldindata.org/life-expectancy#all-charts) dataset from Our World in Data. ```{r} le_df <- read_csv("data/life-expectancy.csv") paged_table(le_df) ``` ### Cleaning life expectancy data ```{r} le_clean <- janitor::clean_names(le_df) # rename column le_clean <- le_clean %>% rename(period_life_expect = period_life_expectancy_at_birth_sex_all_age_0) paged_table(le_clean) ``` ## Global Vaccination Coverage Data ```{r} vaccination_df <- read_csv("data/global-vaccination-coverage.csv") vaccination_df %>% paged_table() ``` ### Cleaning vaccination data ```{r} vaccination_clean <- janitor::clean_names(vaccination_df) %>% paged_table() vaccination_clean ``` # Impact of vacccination Using a combination of Pearson's correlation coefficient and linear regression, the vaccines for one dose of Salk's polio vaccine, one dose of the meningococcal vaccine, three doses of the polio vaccine and three doses of the DPT (diphtheria, pertussis, tetanus) vaccine were found to be the most significant in increasing life expectancy globally. ```{r} merged_datasets = merge(vaccination_clean, le_clean, by=c("entity","year")) names(merged_datasets)[names(merged_datasets) == 'period_life_expectancy_at_birth_sex_all_age_0'] <- 'life_expectancy' ``` ::: # References